Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points

doi:10.11947/j.AGCS.2016.20160372

Abstract

Abstract: Primitive, being the basic processing unit, is one of the key factors to determine the accuracy and efficiency of point cloud filtering. Triangular irregular network (TIN) progressive densification (TPD) and object-based TIN progressive densification (OTPD) are two existing filtering methods, but single primitive is employed by them. A multiple-primitives-based TIN progressive densification (MPTPD) filtering method is proposed. It is composed of three key stages, including point cloud segmentation, extraction of key points of objects, the key-points-based judging of the objects. Specifically, point, object and the key points are the primitive of the above three stages respectively. Four testing datasets, including two airborne LiDAR and two photogrammetric point clouds, are used to verify the overall performances of the above three filtering methods. Experimental results suggest that the proposed MPTPD has the best overall performance. In the viewpoint of accuracy, MPTPD and OTPD have the similar accuracy. Moreover, compared with the TPD, MPTPD is able to reduce omission errors and total errors by 22.07% and 8.44% respectively. In the viewpoint of efficiency, under most of the cases, TPD is the highest, MPTPD is the second, and OTPD is the slowest. Moreover, the total time cost of MPTPD is only 57.93% of the one of OTPD.

Key words: filtering, LiDAR point cloud, photogrammetric point cloud, objects, triangular irregular network

CLC Number:

P237

LIN Xiangguo, ZHANG Jixian, NING Xiaogang, DUAN Minyan, ZANG Yi. Filtering of Point Clouds Using Fusion of Three Types of Primitives Including Points, Objects and Key Points[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(11): 1308-1317.

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